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international conference on Informatics, Electronics & Vision (ICIEV)

作者： Md. Mahamudul Hasan Shuvo Sen Md. Juwel Rana Bikash Kumar Paul Md. Ahsan Habib Golam Moktader Daiyan Kawsar Ahmed Dept. of Physics Mawlana Bhashani Science and Technology University Tangail Bangladesh Dept. of ICT Mawlana Bhashani Science and Technology University Tangail Bangladesh Dept. of Software Engineering (SWE) Daffodil International University Dhaka Bangladesh School of Computer Science and Technology University of Electronic Science and Technology Sichuan Province China Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Tangail Bangladesh

In this article, here is developed a novel TOPAS based heptagonal photonic crystal fiber (H-PCF) for the identification of various chemical substances in the terahertz frequency territory. the full-vector finite element method (FV-FEM) is used for the numerical investigation about the competence of the proposed PCF. Simulation results show the maximum relative sensitivity as well as less connement loss for various chemicals. Numerically processed outcomes apparent that the proposed sensor shows 63.24 %, 61.05 % and 60.03 % detecting reactions for focused analytes benzene, ethanol and water individually at 1 thz frequency. Major purposes is to use H-PCF as the application of chemical sensor in commercial sector to detect harmful chemicals.

关键词： Sensitivity Ethanol Sensors Chemicals Terahertz radiation Optical losses Photonic crystals

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Anomaly Detection Approaches for Secure Cloud Reference Architectures in Legal Metrology 8

Anomaly Detection Approaches for Secure Cloud Reference Arch...

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8th international conference on Cloud computing and Services Science (CLOSER)

作者： Oppermann, Alexander Toro, Federico Grasso thiel, Florian Seifert, Jean-Pierre Phys Tech Bundesanstalt PTB Dept Metrol IT 8 5 Berlin Germany Tech Univ Berlin Secur Telecommun Berlin Germany

ISBN: (纸本)9789897582950

Securing Computer Systems against all kind of threats is an impossible challenge to fulfill. Although, in the field of Legal Metrology, it shall be assured that one can rely on the measurement carried out by a trusted computer system. In a distributed environment, a measurement instrument cannot be simply disconnected to gurantee its security. However, being able to monitor the computer systems constantly in order to deduce a normal system behaviour, can be a particular promising approach to secure such systems. In cases of detected anomalies, the system evaluates them to measure the severity of the detected incident and place it into three different categories: green, yellow and red. the presented Anomaly Detection Module can detect attacks against distributed applications in an cloud computing environment, using pattern recognition for clustering as well as statistical approaches. Both, inexperienced and experienced attacks have been tested and results are presented.

关键词： Anomaly Detection Continous Monitoring Secure Cloud computing Anomaly Detection as a Service Distributed computing Legal Metrology

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the OMG-Empathy Dataset: Evaluating the Impact of Affective Behavior in Storytelling

The OMG-Empathy Dataset: Evaluating the Impact of Affective ...

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international conference on Affective computing and Intelligent Interaction and Workshops, ACII

作者： Pablo Barros Nikhil Churamani Angelica Lim Stefan Wermter Knowledge Technology University of Hamburg Hamburg Germany University of Cambridge Cambridge UK School of Computing Science Simon Fraser University Burnaby BC Canada

Processing human affective behavior is important for developing intelligent agents that interact with humans in complex interaction scenarios. A large number of current approaches that address this problem focus on classifying emotion expressions by grouping them into known categories. Such strategies neglect, among other aspects, the impact of the affective responses from an individual on their interaction partner thus ignoring how people empathize towards each other. this is also reflected in the datasets used to train models for affective processing tasks. Most of the recent datasets, in particular, the ones which capture natural interactions (“in-the-wild” datasets), are designed, collected, and annotated based on the recognition of displayed affective reactions, ignoring how these displayed or expressed emotions are perceived. In this paper, we propose a novel dataset composed of dyadic interactions designed, collected and annotated with a focus on measuring the affective impact that eight different stories have on the listener. Each video of the dataset contains around 5 minutes of interaction where a speaker tells a story to a listener. After each interaction, the listener annotated, using a valence scale, how the story impacted their affective state, reflecting how they empathized with the speaker as well as the story. We also propose different evaluation protocols and a baseline that encourages participation in the advancement of the field of artificial empathy and emotion contagion.

关键词： Emotion recognition Computational modeling Human computer interaction Multimedia computing Intelligent agents

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Audiovisual Analysis for Recognising Frustration during Game-Play: Introducing the Multimodal Game Frustration Database

Audiovisual Analysis for Recognising Frustration during Game...

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international conference on Affective computing and Intelligent Interaction and Workshops, ACII

作者： Meishu Song Zijiang Yang Alice Baird Emilia Parada-Cabaleiro Zixing Zhang Ziping Zhao Björn Schuller Universität Augsburg Germany GLAM - Group on Language Audio & Music Imperial College London UK Center of Affective Computing Tianjin Normal University

Automatic recognition of frustration, by analysing facial and vocal expressions, can help user experience designers to identify interaction obstacles. To encourage the development of automated systems such as these, we present a novel audiovisual database: the Multimodal Game Frustration Database (MGFD), consisting of ca. 5 hours of audiovisual data, collected from 67 Chinese students speaking in English. For data collection, we developed `Crazy Trophy', a Wizard-of-Oz voice activated web-game designed with a variety of usability problems and aimed to induce increasing amounts of frustration. We also present a baseline for binary multimodal frustration classification (frustration vs no-frustration). For this, we compare the performance of a conventional method, Support Vector Machine classifier, and a state-of-the-art method utilising Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), extracting both audio (Mel-frequency Cepstral Coefficients) and video (facial action units) features. Using LSTM-RNN and a feature-based multi-model fusion strategy, the best result acheived for the baseline was 60.3 % UAR. To enable further research in this area, the game (`Crazy Trophy'), the database (MGFD), and the partitioning considered in the presented baseline, are made accessible to the research community.

关键词： Games Databases Task analysis Usability Affective computing Feature extraction Face recognition

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A Universal Way to Collect and Process Handwritten Data for Any Language 8

A Universal Way to Collect and Process Handwritten Data for ...

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8th international conference on Advances in computing & Communications (ICACC)

作者： Rabby, A. K. M. Shahariar Azad Haque, Sadeka Shahinoor, Shammi Akther Abujar, Sheikh Hossain, Syed Akhter Daffodil Int Univ Dept Comp Sci & Engn Dhaka 1205 Bangladesh

In recent years researches based on Machine learning and Deep learning have achieved much interest and one of its handwritten recognition. Handwritten recognition is very difficult due to its lack of dataset and also for collecting data from people. this research introduces a fast and comprehensive way to collect and process handwritten data to develop a way of Handwritten recognition (HWR) algorithm for any languages. In this research handwritten characters wrote on a paper and then scanned to get the data into a JPEG format. We also focused on some of the other issues and requirements while collecting handwritten data, creating form, data collection methodology, process, using software and relevant tools. We described these issues in the context of our own effort to create a handwritten database for the Bangla language. Our designed Graphical User Interface(GUI) is also able to process 100 scanned images per minute where each scanned image contains 120 characters. (C) 2018 the Authors. Published by Elsevier B.V.

关键词： Handwritten recognition pattern recognition Document Image Analysis Machine Learning Computer Vision

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Promoting Common Understanding on Research Data Management using Rubric

Promoting Common Understanding on Research Data Management u...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Takaaki Aoki Shoji Kajita Tamaki Motoki Toshihiko Iyemori Tomoko Kawaguchi Academic Center for Computing and Media Studies Kyoto University Kyoto Japan

Research data management (RDM) is the activity to describe and practice what kind of data is to be used/obtained/generated, and how that data is analyzed/saved/shared or published from the plan to completion of the research project. the RDM consists ambiguous concept so that the understanding of RDM is diverse according to each researcher. In order to foster a common recognition of RDM in a Japanese university, a workshop that researchers utilize a rubric to evaluate and review their attitudes to RDM was held. In this paper, we report the process of the development of RDM rubric and the workshop.

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Business Models for Emerging Technologies: the Case of Affective computing

Business Models for Emerging Technologies: The Case of Affec...

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international conference on Affective computing and Intelligent Interaction Workshops and Demos (ACIIW)

作者： Olga Ignatyeva Dmitry Sokolov Olga Lukashenko Aleksandra Shalakitskaia Sebastian Denef Tatjana Samsonowa IPERF International Institute for Research Performance and Innovation Management e.V Berlin Germany Graduate School of Management St. Petersburg State University St. Petersburg Russia OWN.space Berlin Germany

this paper explores the key features of disruptive business models in the affective computing industry, where particular business benefit and technological value are yet to be discovered. We designed the business model canvas for emotion recognition (ER) solutions based on analysis of 80 companies from secondary sources, as well as six case studies that we conducted ourselves. Based on these insights, our research highlights the elements in each block of the business model canvas that are typical for emerging technologies, namely not fully understood value proposition, B2B customer segment, free demos and trials in customer relationship, SaaS and technology licensing in revenue streams, investors and academia as key partners and R&D as key activity. We designed the canvas as a tool useful for anybody creating or analyzing ventures in emerging tech industries and, in particular, ER solutions. Our study contributes to the existing literature on disruptive business models for technological innovations by consolidating existing practices of ER sector and emphasizing on important patterns for emerging tech industries in general.

关键词： Erbium Companies Biological system modeling Industries Affective computing Computational modeling

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Unconstrained Face recognition using ASURF and Cloud-Forest Classifier optimized with VLAD 8

Unconstrained Face Recognition using ASURF and Cloud-Forest ...

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8th international conference on Advances in computing & Communications (ICACC)

作者： Vinay, A. Joshi, Aviral Surana, Hardik Mahipal Garg, Harsh Murthy, K. N. Balasubramanya Natarajan, S. PES Univ Ctr Pattern Recognit & Machine Intelligence Bangalore 560085 Karnataka India

the paper posits a computationally-efficient algorithm for multi-class facial image classification in which images are constrained with translation, rotation, scale, color, illumination and affine distortion. the proposed method is divided into five main building blocks including Haar-Cascade for face detection, Bilateral Filter for image preprocessing to remove unwanted noise, Affine Speeded-Up Robust Features (ASURF) for keypoint detection and description, Vector of Locally Aggregated Descriptors (VLAD) for feature quantization and Cloud Forest for image classification. the proposed method aims at improving the accuracy and the time taken for face recognition systems. the usage of the Cloud Forest algorithm as a classifier on three benchmark datasets, namely the FACES95, FACES96 and ORL facial datasets, showed promising results. the proposed methodology using Cloud Forest algorithm successfully improves the recognition model by 2-12% when differentiated against other ensemble techniques like the Random Forest classifier depending upon the dataset used. (C) 2018 the Authors. Published by Elsevier B.V.

关键词： Face recognition Haar Cascade Bilateral Filter ASURF Bag of Words VLAD Cloud Forest Random Forest

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Wireless Link Quality Prediction in IoT Networks

Wireless Link Quality Prediction in IoT Networks

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international conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN)

作者： Miguel Landry Foko Sindjoung Pascale Minet Faculty of Science University of Dschang Dschang BP Cameroon Inria 2 rue Simone Iff Paris Cedex 12 France

ISBN: (纸本)9781728125879

the knowledge of link quality in IoT networks allows a more accurate selection of wireless links to build the routes used for data gathering. the number of retransmissions is decreased, leading to a shorter end-to-end latency, a better end-to-end reliability and a larger network lifetime. We propose to predict link quality by means of machine learning techniques applied on two metrics: RSSI and PDR. the accuracy got by Logistic Regression, Linear Support Vector Machine, Support Vector Machine and Random Forest classifier is computed on the traces of a real IoT network deployed in Grenoble.

关键词： computer network reliability Internet of things pattern classification radio links random forests regression analysis RSSI support vector machines computer network reliability Radio links Received Signal Strength Indicator Internet of things Support Vector Network pattern recognition regression analysis wireless

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Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views

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international JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2019年第9期14卷 1463-1473页

作者： Bier, Bastian Goldmann, Florian Zaech, Jan-Nico Fotouhi, Javad Hegeman, Rachel Grupp, Robert Armand, Mehran Osgood, Greg Navab, Nassir Maier, Andreas Unberath, Mathias Johns Hopkins Univ Comp Aided Med Procedures Baltimore MD 21218 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Friedrich Alexander Univ Erlangen Nurnberg Pattern Recognit Lab Erlangen Germany Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA Johns Hopkins Univ Hosp Dept Orthoped Surg Baltimore MD USA

Purpose Minimally invasive alternatives are now available for many complex surgeries. these approaches are enabled by the increasing availability of intra-operative image guidance. Yet, fluoroscopic X-rays suffer from projective transformation and thus cannot provide direct views onto anatomy. Surgeons could highly benefit from additional information, such as the anatomical landmark locations in the projections, to support intra-operative decision making. However, detecting landmarks is challenging since the viewing direction changes substantially between views leading to varying appearance of the same landmark. therefore, and to the best of our knowledge, view-independent anatomical landmark detection has not been investigated yet. Methods In this work, we propose a novel approach to detect multiple anatomical landmarks in X-ray images from arbitrary viewing directions. To this end, a sequential prediction framework based on convolutional neural networks is employed to simultaneously regress all landmark locations. For training, synthetic X-rays are generated with a physically accurate forward model that allows direct application of the trained model to real X-ray images of the pelvis. View invariance is achieved via data augmentation by sampling viewing angles on a spherical segment of 120 circle x90 circle. Results On synthetic data, a mean prediction error of 5.6 +/- 4.5 mm is achieved. Further, we demonstrate that the trained model can be directly applied to real X-rays and show that these detections define correspondences to a respective CT volume, which allows for analytic estimation of the 11 degree of freedom projective mapping. Conclusion We present the first tool to detect anatomical landmarks in X-ray images independent of their viewing direction. Access to this information during surgery may benefit decision making and constitutes a first step toward global initialization of 2D/3D registration without the need of calibration. As such, the proposed conce

关键词： Anatomical landmarks Convolutional neural networks 2D 3D registration Landmark detection

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